A minimalistic but flexible framework that facilitates the creation, management and training of multiple 'caret' models. A model grid consists of two components, (1) a set of settings that is shared by all models by default, and (2) specifications that apply only to the individual models. When the model grid is trained, model and training specifications are first consolidated from the shared and the model specific settings into complete 'caret' model configurations. These models are then trained with the 'train()' function from the 'caret' package.